{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "\n",
    "from torch.distributions.categorical import Categorical\n",
    "from typing import List\n",
    "from tqdm import tqdm\n",
    "\n",
    "import sys\n",
    "sys.path.insert(1, '../src')\n",
    "from config import *\n",
    "from alphaclass import AlphaGFN\n",
    "from models import TBModel\n",
    "from reward import compute_log_reward\n",
    "from loss import trajectory_balance_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Utils\n",
    "def expr_sequence_to_string(expr: List[str]) -> str:\n",
    "    string = ''\n",
    "    stack = []\n",
    "    for a in expr:\n",
    "        if a in ['BEG', 'SEP']:\n",
    "            continue\n",
    "        elif a in FEATURES:\n",
    "            stack.append(a)\n",
    "        elif a in UNARY:\n",
    "            operand = stack.pop()\n",
    "            stack.append(a + '(' + operand + ')')\n",
    "        elif a in BINARY:\n",
    "            operand_2 = stack.pop()\n",
    "            operand_1 = stack.pop()\n",
    "            stack.append(a + '(' + operand_1 + ',' + operand_2 + ')')\n",
    "        else:\n",
    "            ValueError\n",
    "    return stack[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████| 1000/1000 [41:55<00:00,  2.52s/it]\n"
     ]
    }
   ],
   "source": [
    "# Reproducibility.\n",
    "seed = 2024\n",
    "torch.manual_seed(seed)\n",
    "random.seed(seed)\n",
    "np.random.seed(seed)\n",
    "torch.manual_seed(seed)\n",
    "# torch.backends.cudnn.deterministic = True\n",
    "# torch.backends.cudnn.benchmark = False\n",
    "\n",
    "# Instantiate model and optimizer\n",
    "model = TBModel(num_hid_1 = 128, num_hid_2 = 128)\n",
    "opt = torch.optim.Adam(model.parameters(),  learning_rate)\n",
    "\n",
    "# Accumulate losses here and take a gradient step every `update_freq` episode (at the end of each trajectory).\n",
    "losses, sampled_ics, sampled_expressions, logZs = [], [], [], []\n",
    "minibatch_loss = torch.tensor([0], dtype=torch.float32)\n",
    "update_freq = 20\n",
    "\n",
    "n_episodes = 1000\n",
    "\n",
    "alpha = AlphaGFN()\n",
    "\n",
    "for episode in tqdm(range(n_episodes), ncols=100):\n",
    "    \n",
    "    alpha.reset()  # Each episode starts with an empty state and operand stack.\n",
    "    P_F_s, P_B_s = model(alpha.get_tensor_state().unsqueeze(0))  # Forward and backward policy\n",
    "    total_log_P_F, total_log_P_B = 0, 0\n",
    "\n",
    "    for t in range(MAX_EXPR_LENGTH):  # All trajectories are at most length MAX_EXPR_LENGTH.\n",
    "\n",
    "        # Here we mask the relevant forward actions.\n",
    "        mask = alpha.get_forward_masks()\n",
    "        P_F_s = torch.where(mask, P_F_s, torch.tensor(-100, dtype=torch.float32))  # Removes invalid forward actions.\n",
    "\n",
    "        # Here P_F is logits, so we use Categorical to compute a softmax.\n",
    "        categorical = Categorical(logits=P_F_s)\n",
    "        action = categorical.sample()  # Sample the next action.\n",
    "        alpha.step(action)  # Update to new state.\n",
    "        total_log_P_F += categorical.log_prob(action)  # Accumulate the log_P_F sum.\n",
    "\n",
    "        # We recompute P_F and P_B for the new state.\n",
    "        P_F_s, P_B_s = model(alpha.get_tensor_state().unsqueeze(0))\n",
    "        mask = alpha.get_backward_masks()\n",
    "        P_B_s = torch.where(mask, P_B_s, torch.tensor(-100, dtype=torch.float32))  # Removes invalid backward actions.\n",
    "\n",
    "        # Accumulate P_B, going backwards from new state. \n",
    "        total_log_P_B += Categorical(logits=P_B_s).log_prob(action)\n",
    "        \n",
    "        if alpha._action_to_token(action) == \"SEP\":  # End of trajectory.\n",
    "            break\n",
    "\n",
    "    if alpha._action_to_token(action) == \"SEP\":\n",
    "        ic, log_reward = compute_log_reward(alpha.stack[0])\n",
    "        if not np.isnan(log_reward):\n",
    "            minibatch_loss += trajectory_balance_loss(\n",
    "                model.logZ,\n",
    "                total_log_P_F,\n",
    "                total_log_P_B,\n",
    "                log_reward = torch.tensor(log_reward, dtype=torch.float32),\n",
    "            )\n",
    "\n",
    "            sampled_expressions.append([alpha._action_to_token(action) for action in alpha.state])\n",
    "            sampled_ics.append(ic) \n",
    "        \n",
    "    if episode > 0 and episode % update_freq == 0 and minibatch_loss:\n",
    "        losses.append(minibatch_loss.item())\n",
    "        logZs.append(model.logZ.item())\n",
    "        minibatch_loss.backward()\n",
    "        opt.step()\n",
    "        opt.zero_grad()\n",
    "        minibatch_loss = 0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examine training results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Since we only trained for 1000 episodes here, the model has not been optimized at all. The following code chunks are only for illustration purpose!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loss curve and estimated partition function over time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a propoerly trained model that has sufficient exploration, the loss should decrease over time and estimated partition function Z should reach a plateau."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Loss and Estimated Partition Function for the Trajectory Balance Model')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 1, figsize=(10, 6))\n",
    "plt.sca(ax[0])\n",
    "plt.plot(losses, color=\"black\")\n",
    "plt.yscale('log')\n",
    "plt.ylabel('Loss')\n",
    "plt.sca(ax[1])\n",
    "plt.plot(np.exp(logZs), color=\"black\")\n",
    "plt.ylabel('Estimated Z');\n",
    "plt.xlabel('Step')\n",
    "plt.suptitle(\"Loss and Estimated Partition Function for the Trajectory Balance Model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example expressions and their IC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe tex2jax_ignore\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>expr</th>\n",
       "      <th>ic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>ops_divide(ops_subtract(ops_divide(ops_add(ops_subtract(ops_divide($open,ops_roll_std($volume)),$open),ops_roll_std($close)),ops_abs($high)),$close),$open)</td>\n",
       "      <td>-0.018810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>ops_roll_std(ops_roll_std(ops_divide(ops_add(ops_abs($volume),ops_abs($open)),$volume)))</td>\n",
       "      <td>-0.017378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>ops_roll_std(ops_multiply(ops_add(ops_subtract(ops_divide(ops_roll_corr($high,$open),$open),$low),$open),$open))</td>\n",
       "      <td>-0.016817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>ops_abs(ops_divide(ops_multiply(ops_abs(ops_roll_corr($open,$open)),ops_abs(ops_roll_std(ops_abs($open)))),$volume))</td>\n",
       "      <td>-0.016738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>ops_divide(ops_subtract(ops_add(ops_subtract(ops_subtract(ops_roll_std(ops_divide(ops_roll_std($open),$volume)),$high),$volume),$high),ops_roll_std(ops_roll_std($open))),$open)</td>\n",
       "      <td>-0.016213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>ops_abs(ops_subtract(ops_divide(ops_subtract(ops_divide(ops_log($high),ops_roll_std(ops_roll_std($low))),$volume),$open),$close))</td>\n",
       "      <td>0.016005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>ops_subtract(ops_divide(ops_subtract(ops_abs($volume),ops_abs(ops_roll_std(ops_log($volume)))),$high),$high)</td>\n",
       "      <td>0.016217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>ops_roll_std(ops_subtract(ops_divide(ops_divide(ops_abs($volume),ops_roll_std(ops_roll_std($open))),ops_roll_std($close)),$high))</td>\n",
       "      <td>0.016481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>ops_add(ops_multiply(ops_abs(ops_divide(ops_roll_corr($open,ops_log($open)),ops_abs(ops_roll_std($open)))),$volume),$open)</td>\n",
       "      <td>0.016710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>ops_multiply(ops_roll_corr($low,$open),$volume)</td>\n",
       "      <td>0.017828</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>882 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                                 expr  \\\n",
       "493                       ops_divide(ops_subtract(ops_divide(ops_add(ops_subtract(ops_divide($open,ops_roll_std($volume)),$open),ops_roll_std($close)),ops_abs($high)),$close),$open)   \n",
       "404                                                                                          ops_roll_std(ops_roll_std(ops_divide(ops_add(ops_abs($volume),ops_abs($open)),$volume)))   \n",
       "399                                                                  ops_roll_std(ops_multiply(ops_add(ops_subtract(ops_divide(ops_roll_corr($high,$open),$open),$low),$open),$open))   \n",
       "736                                                              ops_abs(ops_divide(ops_multiply(ops_abs(ops_roll_corr($open,$open)),ops_abs(ops_roll_std(ops_abs($open)))),$volume))   \n",
       "269  ops_divide(ops_subtract(ops_add(ops_subtract(ops_subtract(ops_roll_std(ops_divide(ops_roll_std($open),$volume)),$high),$volume),$high),ops_roll_std(ops_roll_std($open))),$open)   \n",
       "..                                                                                                                                                                                ...   \n",
       "448                                                 ops_abs(ops_subtract(ops_divide(ops_subtract(ops_divide(ops_log($high),ops_roll_std(ops_roll_std($low))),$volume),$open),$close))   \n",
       "606                                                                      ops_subtract(ops_divide(ops_subtract(ops_abs($volume),ops_abs(ops_roll_std(ops_log($volume)))),$high),$high)   \n",
       "577                                                 ops_roll_std(ops_subtract(ops_divide(ops_divide(ops_abs($volume),ops_roll_std(ops_roll_std($open))),ops_roll_std($close)),$high))   \n",
       "824                                                        ops_add(ops_multiply(ops_abs(ops_divide(ops_roll_corr($open,ops_log($open)),ops_abs(ops_roll_std($open)))),$volume),$open)   \n",
       "225                                                                                                                                   ops_multiply(ops_roll_corr($low,$open),$volume)   \n",
       "\n",
       "           ic  \n",
       "493 -0.018810  \n",
       "404 -0.017378  \n",
       "399 -0.016817  \n",
       "736 -0.016738  \n",
       "269 -0.016213  \n",
       "..        ...  \n",
       "448  0.016005  \n",
       "606  0.016217  \n",
       "577  0.016481  \n",
       "824  0.016710  \n",
       "225  0.017828  \n",
       "\n",
       "[882 rows x 2 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_colwidth', 200)\n",
    "pd.set_option('display.html.use_mathjax', False)\n",
    "res = pd.DataFrame(\n",
    "    {'expr': [expr_sequence_to_string(ex) for ex in sampled_expressions], 'ic': sampled_ics}\n",
    ").sort_values(by='ic')\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reset index to create a new column with original index values\n",
    "res['original_index'] = res.index\n",
    "res0 = res.sort_values(by='original_index')\n",
    "ic_ma = res0['ic'].rolling(window=100).mean()\n",
    "plt.plot(range(len(ic_ma)), np.abs(ic_ma), label='Line')\n",
    "plt.xlabel('Trajectory index (ordered by time)')\n",
    "plt.ylabel('Absolute IC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'ic'}>]], dtype=object)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res.hist('ic', bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zooming-in: groups of expressions with high absolute ICs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Negative IC\n",
    "# IC < -0.01\n",
    "expr_g1 = [sampled_expressions[i] for i, ic in enumerate(sampled_ics) if ic < -0.01]\n",
    "act_g1  = [a for expr in expr_g1 for a in expr]\n",
    "## Positive IC\n",
    "# IC >= 0.01\n",
    "expr_g2 = [sampled_expressions[i] for i, ic in enumerate(sampled_ics) if ic >= 0.01]\n",
    "act_g2  = [a for expr in expr_g2 for a in expr]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "count_g1 = Counter(act_g1).most_common()\n",
    "count_g2 = Counter(act_g2).most_common()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels, counts = zip(*count_g1)\n",
    "plt.bar(labels, counts); plt.xticks(rotation=90)\n",
    "plt.title('Frequency of actions in expresson with IC < -0.05')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels, counts = zip(*count_g2)\n",
    "plt.title('Frequency of actions in expresson with -0.05 <= IC< -0.04')\n",
    "plt.bar(labels, counts); plt.xticks(rotation=90); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zooming-in: Groups of expressions with low absolute ICs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# |IC| < 0.005\n",
    "expr_g3 = [sampled_expressions[i] for i, ic in enumerate(sampled_ics) if np.abs(ic) < 0.005]\n",
    "act_g3  = [a for expr in expr_g3 for a in expr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_g3 = Counter(act_g3).most_common()\n",
    "labels, counts = zip(*count_g3)\n",
    "plt.bar(labels, counts); plt.xticks(rotation=90)\n",
    "plt.title('Frequency of actions in expresson with |IC| < 0.01')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "assignment_venv",
   "language": "python",
   "name": "assignment_venv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
